13 March 2017 Quantitative analysis of structural variations in corpus callosum in adults with multiple system atrophy (MSA)
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Abstract
Multiple system atrophy (MSA) is a rare, non-curable, progressive neurodegenerative disorder that affects nervous system and movement, poses a considerable diagnostic challenge to medical researchers. Corpus callosum (CC) being the largest white matter structure in brain, enabling inter-hemispheric communication, quantification of callosal atrophy may provide vital information at the earliest possible stages. The main objective is to identify the differences in CC structure for this disease, based on quantitative analysis on the pattern of callosal atrophy. We report results of quantification of structural changes in regional anatomical thickness, area and length of CC between patient-groups with MSA with respect to healthy controls. The method utilizes isolating and parcellating the mid-sagittal CC into 100 segments along the length - measuring the width of each segment. It also measures areas within geometrically defined five callosal compartments of the well-known Witelson, and Hofer-Frahma schemes. For quantification, statistical tests are performed on these different callosal measurements. From the statistical analysis, it is concluded that compared to healthy controls, width is reduced drastically throughout CC for MSA group and as well as changes in area and length are also significant for MSA. The study is further extended to check if any significant difference in thickness is found between the two variations of MSA, Parkinsonian MSA and Cerebellar MSA group, using the same methodology. However area and length of this two sub-MSA group, no substantial difference is obtained. The study is performed on twenty subjects for each control and MSA group, who had T1-weighted MRI.
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Debanjali Bhattacharya, Neelam Sinha, Jitender Saini, "Quantitative analysis of structural variations in corpus callosum in adults with multiple system atrophy (MSA)", Proc. SPIE 10137, Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging, 101371Z (13 March 2017); doi: 10.1117/12.2253873; https://doi.org/10.1117/12.2253873
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